Wikipedia Word Analyzer

URL → scrape → tokenize → visualize top frequent words.

PythonFlaskBeautifulSoupNLTKChart.js

Interactive Demo (Simulated)

learning

142

machine

128

data

algorithms

model

training

neural

features

classification

prediction

NLP Pipeline

Fetch HTML

requests.get(url)

Extract Text

#mw-content-text

Tokenize & Filter

NLTK stopwords

Visualize

Chart.js bars

Extended Stopwords

Standard NLTK stopwords aren't enough for Wikipedia. Added domain-specific terms:

theisatwikipediacitereferenceretrievedarchiveeditpageexternallinks

HTML Exclusions

Elements removed before text extraction:

table.infoboxSidebar info boxes

sup.referenceCitation brackets

div.navboxNavigation templates

#ReferencesReferences section

Design Decisions

BeautifulSoup over Wikipedia API

More control over content extraction — can exclude specific sections that would skew analysis.

Stateless Processing

No database, no persistence. Each request processed fresh for simplicity and easy deployment.

Top-N Frequency Limit

Show 20-30 terms max. Long-tail words (1-2 occurrences) add noise without insight.

Speed over Perfect Accuracy

Simple tokenization beats advanced NLP. 80% accuracy in milliseconds > 95% in seconds.

<1s

Analysis Time

Top Words Shown

150+

Stopwords Filtered

Flask

Backend Framework